For an article about LLM+OLAP, it doesn't spend much time on that part. Specifically it seems like their strategy is around using an LLM to generate a DSL query for an unnamed semantic layer, then everything downstream of that is normal warehousing, with the semantic layer handling actual SQL creation.
I wish it spent time on talking about how they trained their LLM to reliably generate parsable queries for the semantic layer, and what the accuracy rate of what the user intended vs what they got.
I do think the only way a LLM based analytics tool can succeed is via a semantic layer rather than direct SQL, since database schemas fail to encode a lot of information about the data (EG a warehouse might not even know user.customer_id = customer.id).
I wish it spent time on talking about how they trained their LLM to reliably generate parsable queries for the semantic layer, and what the accuracy rate of what the user intended vs what they got.
I do think the only way a LLM based analytics tool can succeed is via a semantic layer rather than direct SQL, since database schemas fail to encode a lot of information about the data (EG a warehouse might not even know user.customer_id = customer.id).
Malloy could be an interesting target here.